We hypothesize in Section 3, however, that the task is likely to succumb to a better solution by treating it in the context of a new machine learning setting that we refer to as partially supervised clustering.

due to high coreference annotation costs, data sets that are annotated with opinion information (like ours) do not typically include supervisory coreference information for all noun phrases in a document (as would be required for the application of traditional coreference resolution techniques), but only for noun phrases that act as opinion sources (or targets).


The property of partially supervised clustering that we want to explore is the structured nature of the decisions.

each decision of whether two items (say a and b) belong to the same cluster has an implication for all items a that belong to a's cluster and all items b that belong to b's cluster.


We compare the results of the new method to three fully supervised baseline systems, each of which employs the same traditional coreference resolution approach.

we use the aforementioned algorithm proposed by Ng and Cardie (2002), which combines a pairwise NP coreference classifier with single-link clustering.


In addition to the baselines described above, we evaluate StRip both with and without unlabeled data.

we train on the MPQA corpus StRip using either all NPs or just opinion source NPs.


If no proposition is detected as in a nominal sentence, the context is split.

there is no link between the proposition of the previous sentence and that of the next sentence.


On the other hand, meetings present a rich source of structural and pragmatic information that makes summarization of multi-party speech quite unique.

our analyses of patterns in the verbal exchange between participants found that adjacency pairs (AP), a concept drawn from the conversational analysis literature (Schegloff and Sacks, 1973), have particular relevance to summarization.


While exact inference might be possible in our case, we used the simpler approach of adapting standard inference algorithms for linear-chain models.

to account for skip-edges, we used a technique inspired by (Sha and Pereira, 2003), in which multiple state dependencies, such as an order-2 Markov model, are encoded using auxiliary tags.


Since BNs and CRFs are here parameterized as log-linear models and rely on the same set of feature functions, a second approach is to use CRF-trained model parameters to build a BN classifier that assigns a probability to each y t .

the CRF model is first used to generate label predicitonsŷ, from which the locally-normalized model estimates the cost of predictingŷ t = 1 given a label historyŷ 1:t−1 .


The information retrieval feature set contains many features that are generally found helpful in summarization, in particular tf •idf and scores derived from centroid methods.

we used the latent semantic analysis (LSA) feature discussed in (Murray et al., 2005), which attempts to determine sentence importance through singular value decomposition, and whose resulting singular values and singular vectors can be exploited to associate each utterance a degree of relevance to one of the top-n concepts of the meetings (where n represents the number of dimensions in the LSA).


The cache intuition has been extended by attempting to increase the probability of unobserved but topically related words.

given a mixture model with topic-specific components, we can increase the mixture weights of the topics corresponding to previously observed words to better predict the next word.


This particular implementation performs Gibbs sampling, a form of Markov chain Monte Carlo (MCMC), to estimate the optimal model parameters fitted to the training data.

the algorithm creates a Markov chain whose stationary distribution matches the expected distribution of the state and topic labels for each word in the training corpus.


It drives its OUTPUT SIGNAL to a value that is the LOGICAL AND of the INPUTS.


An OR GATE is a SIMILAR two INPUT PRIMITIVE FUNCTION box that drives its OUTPUT SIGNAL to a value that is the LOGICAL OR of the INPUTS.

the OUTPUT will BECOME 1 if at least ONE of the INPUT SIGNALS is 1; otherwise the OUTPUT will BECOME 0.  practical.


In general, an n-gram model trained on a limited set of topic-specific documents tends to overemphasize words from the observed topics instead of evenly distributing weights over all potential topics.

given the list of words following an n-gram context, we would like to deemphasize the observed occurrences of topic words and ideally redistribute these counts to all potential topic words.


Instead, we employ an exponential decay strategy where we update the current mixture distribution by linearly interpolating it with the posterior topic distribution given the current word.

applying Bayes' rule, the probability of topic t generating the current word w is given by: To achieve the exponential decay, we update the topic distribution after each word according to P i+1 (t) = (1 -) P i (t) + P(t | w i ), where is the adaptation rate.


Here, we first select the most likely morphological analysis for each word using a morphological  tagger.

we use the Czech featurebased tagger distributed with the Prague Dependency Treebank (Hajič et al., 2005).


Our focus is on investigating several machine learning techniques for this problem.

we argue that lexicon learning in resourcepoor languages can be best viewed as transductive learning.


Although previous research in unsupervised tagging have achieved high accuracies rivaling supervised methods (Kupiec, 1992;Brill, 1995), much of the success is due to the use of artificially constrained lexicons.

the lexicon is a wordlist where each word is annotated with the set of all its possible tags.


In the case of unsupervised HMM taggers, the lexicon provides constraints on the probability tables p(w i |t i ) and p(t i |t i−1 , t i−2 ).

the lexical probability table is initialized such that p(w i |t i ) = 0 if and only if tag t i is not included in the POS-set of word w i .


We use the algorithm as described in (Duh and Kirchhoff, 2005b), which adapts the algorithm to structured output problems.

the modification involves a different estimate of the priors p(h), which was assumed to be uniform in (El-Yaniv and Gerzon, 2005).


We attempted to correct these errors by using POS-sets of words derived from the development data.

of the 1562 partial lexicon words that also occur in the development set, we found 1044 words without entirely matching POS-sets.


So, in this work, we would like to study the problem of partially supervised sense disambiguation with an incomplete sense tagged corpus.

given an incomplete sense-tagged corpus and a large amount of untagged examples for a target word 3 , we are interested in (1) labeling the instances in the untagged corpus with sense tags occurring in the tagged corpus; (2) trying to find undefined senses (or new senses) of the target word 4 from the untagged corpus, which will be represented by instances from the untagged corpus.


In this paper, we present a global inference approach  to the extraction of opinion-related entities and relations.

we aim to identify two types of entities (i.e.


1 In addition, we aim to identify the relations between opinion expression entities and source entities.

for a given opinion expression O i and source entity S j , we determine whether the relation L i,j def = (S j expresses O i ) obtains, i.e.


Inspired by , we model our task as global, constraint-based inference over separately trained entity and relation classifiers.

we develop three base classifiers: two sequence-tagging classifiers for the extraction 1 See  for additional details.


Integer Linear Programming Finally, we formulate an integer linear programming problem for each sentence using the results from the previous two phases.

we specify a number of soft and hard constraints among relations and entities that take into account the confidence values provided by the supporting entity and relation classifiers, and that encode a number of heuristics to ensure coherent output.


(2004); however, our definition of opinion and source entities are much more extensive, going beyond single sentences and propositional opinion expressions.

we evaluate our approach with respect to (1) a wide variety of opinion expressions, (2) explicit and implicit 3 sources, (3) multiple opinion-source link relations per sentence, and (4) link relations that span more than one sentence.


(2005) focus only on the extraction of sources of opinions, without extracting opinion expressions.

kim and Hovy (2005b) assume a priori existence of the opinion expressions and extract a single source for each, while Choi et al.


By the definition of a link, if L i,j = 1, then it is implied that O i = 1 and S j = 1.

if a link is extracted, then the pair of entities for the link must be also extracted.


11 6 Experiments-I We evaluate our system using the NRRC Multi-Perspective Question Answering (MPQA) corpus that contains 535 newswire articles that are manually annotated for opinion-related information.

our gold standard opinion entities correspond to direct subjective expression annotations and subjective speech event annotations (i.e.


Extra SRL Constraints for the ILP phase We also incorporate SRL into the ILP phase of our system by adding extra constraints based on SRL.

we assign very high weights for links that match V-A0 frames generated by SRL, in order to force the extraction of V-A0 frames.


This process brings the F-measure of CRF-OP up to 82.0, with a different precision-recall break down from those of 1-best sequences without ILP phase.

the recall on opinion expressions now reaches 82.3%, while maintaining a high precision of 81.7%.


We regard users' editing histories as implicit ratings.

if users have edited articles, we consider that they have positive attitudes toward the articles.


This implies that the occurrences of users are more clustered than those of words.

the degree of repetition in the editing histories of users is greater than that in word sequences.


Using a semi-CRF as opposed to a traditional Markov CRF allows us to model some aspects of word segmentation that one would expect to be very informative.

it makes possible the use of local indicator function features of the type "the chunk consists of character sequence χ 1 , .


The additional machinery in URES is necessary when redundancy is low.

uRES is more effective in identifying low-frequency instances, due to its more expressive rule representation, and its classifier that inhibits those rules from overgeneralizing.


We performed an experimental comparison between URES, URES-NER and the state-ofthe-art KnowItAll system, and showed that URES can double or even triple the recall achieved by KnowItAll for relatively rare relation instances, and get an additional 5-15% boost in recall by utilizing a simple NER.

we have shown that URES is more effective in identifying low-frequency instances, due to its more expressive rule representation, and its classifier (augmented by NER) that inhibits those rules from overgeneralizing.


In (Corney, 2003;Gamon, 2004;Peng et al., 2004;Koppel and Schler, 2004) the datasets were rather small in terms of the number of authors, indicating the results may not be generalisable.

in (Corney, 2003) the largest dataset contains texts from five authors, in (Gamon, 2004) from three, while in (Peng et al., 2004) and (Koppel and Schler, 2004) from ten.


The intuition is that the tuples matched by many different patterns tend to be correct and the patterns matching many different tuples tend to be good patterns.

; we want to choose, among the large space of patterns in the data, the most informative, highest confidence patterns that could identify correct tuples; i.e.


For instance, in more than eight years of manual construction (from version 1.5 to 2.0), Word-Net passed from 103,445 semantic relations to 204,074 semantic relations 1 .

around twelve thousand semantic relations per year.


The quality of each large-scale knowledge resource is indirectly evaluated on a Word Sense Disambiguation (WSD) task.

we use a well defined WSD evaluation benchmark (Senseval-3 English Lexical Sample task) to evaluate the quality of each resource.


Furthermore, this work studies how these resources complement each other.

to which extent each knowledge base provides new knowledge not provided by the others.


Using this technology, most of these resources are integrated into a common resource called Multilingual Central Repository (MCR) (Atserias et al., 2004).

all WordNet versions, eX-tended WordNet, and the semantic preferences acquired from SemCor and BNC.


• TSWEB 3 : Inspired by the work of (Leacock et al., 1998), these Topic Signatures were constructed using monosemous relatives from WordNet (synonyms, hypernyms, direct and indirect hyponyms, and siblings), querying Google and retrieving up to one thousand snippets per query (that is, a word sense).

the method was as follows: -Organizing the retrieved examples from the web in collections, one collection per word sense.


In this study we consider two different query strategies: • Monosemous A (queryA): (OR monosemous-words).

the union set of all synonym, hyponym and hyperonym words of a WordNet synset which are monosemous nouns (these words can have other senses as verbs, adjectives or adverbs).


• Monosemous W (queryW): (OR monosemous-words).

the union set of all words appearing as synonyms, direct hyponyms, hypernyms indirect hyponyms (distance 2 and 3) and siblings.


In order to measure the quality of the knowledge resources described in the previous section, we performed an indirect evaluation by using all these resources as Topic Signatures (TS).

word vectors with weights associated to a particular synset which are obtained by collecting those word senses appearing in the synsets directly related to them 6 .


All knowledge resources are indirectly evaluated on a WSD task.

the noun-set of Senseval-3 English Lexical Sample task which consists of 20 nouns.


This represents a recall 18.5 points higher than WN.

the knowledge integrated into the MCR (Word-Net, eXtended WordNet and the selectional preferences acquired from SemCor) although partly derived by automatic means performs much better  Table 6: P, R and F1 fine-grained results for the resources integrated into the MCR.


This process will be performed by disambiguating the Topic Signatures.

trying to obtain word sense vectors instead of word vectors.


For example, the (stemmed) expansion terms for the query "insider trading" suggested respectively by CIQE and CDQE are as follows: CIQE: stock:0.0141 market:0.0113 US:0.0112 year:0.0102 exchang:0.0101 trade:0.0092 report:0.0082 price:0.0076 dollar:0.0071 1:0.0069 govern:0.0066 state:0.0065 futur:0.0061 million:0.0061 dai:0.0060 offici:0.0059 peopl:0.0059 york:0.0057 issu:0.0057 … CDQE: secur:0.0161 charg:0.0158 stock:0.0137 scandal:0.0128 boeski:0.0125 inform:0.0119 street:0.0113 wall:0.0112 case:0.0106 year:0.0090 million:0.0086 investig:0.0082 exchang:0.0080 govern:0.0077 sec:0.0077 drexel:0.0075 fraud:0.0071 law:0.0063 ivan:0.0060 … We can see that in general, the terms suggested by CDQE are much more relevant.

it has been able to suggest "boeski" (Boesky) who is involved in an insider trading scandal.


From this table, we can observe that relations extracted from any collection are useful to some degree: they all outperform UM (see Table 2).

the relations extracted from AP are the best for almost all the collections.


With this choice the loss will only be large if the outlier turns out to be correct, while if the outlier is chosen then the loss will be large if any of the group are correct.

the expected loss of choosing a member of the group will be smaller than that for the outlier.


In this paper we show that generative models are competitive with and sometimes superior to discriminative models, when both kinds of models are allowed to learn structures that are optimal for discrimination.

we compare Bayesian Networks and Conditional loglinear models on two NLP tasks.


We ask the question: given the same set of input features, what is the best a generative model can do if it is allowed to learn an optimal structure for the joint distribution, and what is the best a discriminative model can do if it is also allowed to learn an optimal structure.

we do not impose any independence assumptions on the generative or discriminative models and let them learn the best representation of the data they can.


For NLP tasks it has been shown that other smoothing methods are far superior to add-α smoothing -see, for example, Goodman (2001).

it is important to incorporate lower-order information based on subsets of the conditioning information.


Each edge is assigned a weight which measures the relative frequency of the two words cooccurring.

let w ij be the weight of the edge 3 connecting nodes i and j, then where The weight of an edge measures how tightly connected the two words are.


Although PageRank was initially designed to work with directed graphs, and with no weights in links, the algorithm can be easily extended to model undirected graphs whose edges are weighted.

let G = (V, E) be an undirected graph with the set of vertices V and set of edges E. For a given vertex v i , let In(v i ) be the set of vertices pointing to it 5 .


These results also suggest new directions of research within this model.

the labels occurring in each sequence tend to coincide with predicates (verbs) and arguments (nouns and named entities).


One could employ the estimated classifier to convert fields into records as follows: Classify each pair of fields as positive or negative, and perform transitive closure to enforce transitivity of decisions.

if the classifier determines that A and B belong to the same record and that B and C belong to the same record, then by transitivity A and C must belong to the same record.


• McDonald: This method uses the pairwise compatibility function, but instead of calculating the mean of inter-cluster edges, it calculates the geometric mean of all pairs of edges in the potential new cluster.

to calculate the compatibility of records R i and R j , we construct a new record R ij that contains all fields of R i and R j , then calculate the geometric mean of all pairs of fields in R ij .


For these experiments, we compare performance on the true record for each page.

we calculate how often each system returns a complete and accurate extraction of the contact record pertaining to the owner of the webpage.


The extraction context of the transliteration application imposes additional complexity constraints on the task.

we aim for the transliteration speed to be comparable to that of extraction speed.


For each local prediction, we use the history h representing the context of making a single transliteration prediction.

we predict each letter e i based on the pair Formally, we map H×E into a d-dimensional feature space ϕ: H×E → R d , where each ϕ k (h,e)(k∈{1,..,d}) corresponds to a condition defined in terms of the history h and the currently predicted letter e. In order to model string termination, we augment E with a sentinel symbol $, and we append $ to each e from D. Given a transliteration dictionary D, we transform the dictionary in a set of |E| binary learning problems.


The value of a feature is the number of times the condition holds true for a given pair of strings.

for every local feature ϕ k ((e[1,i-1], f),e i ) we can define the corresponding global feature: We seek a transliteration model that is linear in the global features.


However, manual corrections are still needed.

speech recognition errors have to be corrected.


Finally, the framework presented in this paper opens up exciting possibilities for future work.

we aim at automatically transforming report dictations into properly formatted and rephrased reports that conform to the requirements of the relevant domain.


But other types of contradictions can only be detected with the help of a body of background knowledge K: In these cases, T and H alone are mutually consistent.

a contradiction between T and H arises only in the context of K. : Consider the example of Mozart's birthplace in the introduction.


Likewise, for fixed Θ f , for any given x we can compute the expected fraction of locally functional relations R that are locally unambiguous for x.

we repeat until convergence: In both steps above, the sums are taken over only those x or R for which D R(x,•) is non-empty.


Comparison Study To evaluate ReLIE for entity extraction vis-a-vis existing algorithms, we used the popular conditional random field (CRF).

we used the MinorThird (Cohen, 2004) implementation of CRF to train models for all four extraction tasks.


We use the same set of binary features as in previous work on this dataset (Pang et al., 2002;Pang and Lee, 2004;Zaidan et al., 2007).

let V = {v 1 , ..., v 17744 } be the set of word types with count ≥ 4 in the full 2000-document corpus.


Given ( x, y, θ), let us say that a unigram w ∈ x is relevant, irrelevant, or anti-relevant if y • θ w is respectively 0, ≈ 0, or 0.

w is relevant if its presence in x strongly supports the annotated class y, and anti-relevant if its presence strongly supports the opposite class −y.


To illustrate this theoretical result, we followed the generative process as described above, and constructed an artificial dataset with characteristics similar to those of our WAD dataset (see Section 5.1).

we fixed the size of the artificial dataset to be equal to the size of the WAD dataset (N = 330, 000).


First, OCCC tends to outperform LTB only on data chunks where the results are quite low in general (less than 60% accuracy).

on weeks 2, 4, 11, and 16 the LTB models show extremely poor performance.


The second term however can approximated using samples {u i } from the current model - 3 In (Welling et al., 2003) the term for P 0 does not appear, which is equivalent to taking the uniform distribution as the baseline model.

given a set of N samples {u i } from the model, the optimal feature to add is one that gives high scores to sampled sentences and low ones to real sentences.


Rather than solely relying on co-occurrence counts, we would like to use them to generalize to unseen pairs.

we would like to exploit a number of arbitrary and potentially overlapping properties of predicates and arguments when we assign SPs.


every feature is an intersection of the occurrence of a particular predicate,v, and some feature of the argument f (n).


It is interesting to inspect the feature weights returned by our system.

the weights on the verb co-occurrence features (Section 3.3.1) provide a high-quality, argument-specific similarityranking of other verb contexts.


HOLMES makes some important simplifying assumptions.

we use simple ground tuples to represent extracted assertions (e.g., contains(kale, calcium)).


Given a query, expressed as a conjunctive Datalog rule, HOLMES generates a probabilistic model using knowledge-based model construction (KBMC) (Wellman et al., 1992).

hOLMES utilizes fast, logical inference to find the subset of ground assertions and inference rules that may influence the answers to the query -enabling the construction of a small and focused Markov network.


We set the rule weights to capture the intuition that deeper inferences decrease the likelihood (as there are more chances to make mistakes), whereas additional, independent proof trees increase the likelihood (as there is more supporting evidence).

in our experiments we set the prior on inferred facts to -0.75, the weight on rule 1 to 1.5, and the weights on all other rules to 0.6.


These features are defined on source words.

there are two kinds of lexical features: external features f −1 and f +1 , which are the source words immediately to the left and right of f ( T ), respectively; internal features f L (T (X k )) and f R (T (X k )), which are the left most and right most boundary words of the source phrase covered by T (X k ), respectively.


In this paper, we propose a new, coarse-to-fine, multipass approach which allows much greater speedups by translating into abstracted languages.

rather than beginning with a low-order model of a still-large language, we exploit language projections, hierarchical clusterings of the target language, to effectively reduce the size of the target language.


We start by decoding an input x in the simplest state space S 0 .

we compute the chart of the posterior distributions p 0 (s) = P (s|x) for all states s ∈ S 0 .


As we will see in our experiments (Section 5) the largest gains can be obtained with extremely coarse language models.

the largest coarse model we use in our best multipass decoder uses a 4-bit encoding and hence has only 16 distinct words (or at most 4096 trigrams).


We then use EM to learn parameters, which splits the state, and determinize the result.

each word w is assigned to s 1 if P (w|s 1 ) > P (w|s 2 ) and s 2 otherwise.


When different decoder settings are applied to the same model, MERT weights (Och, 2003) from the unprojected single pass setup are used and are kept constant across runs.

the same MERT weights are used for all coarse passes; note that this slightly disadvantages the multipass runs, which use MERT weights optimized for the single pass decoder.


Firstly, the sentiment of each sentence in a discourse is not independent to each other.

the sentiment of each sentence is related to those of other adjacent sentences in the same discourse.


Unfortunately, in our knowledge most of the current research treats sentiment classification as a traditional multi-classification task or an ordinal regression task, which regard the sentimental classes being independent to each other and each sentence is also independent to the adjacent sentences in the context.

they neglect the contextual information and the redundancy among sentiment classes.


In this paper, we propose and evaluate methods that can be employed to transfer a repository of subjectivity resources across languages.

we attempt to leverage on the resources available for English and, by employing machine translation, generate resources for subjectivity analysis in other languages.


Examples include (Joachims, 2002), (Freund et al., 2003) and (Qin et al., 2007).

the training phase of the Joachims' Ranking SVM (Joachims, 2002) is formulated as the following SVM optimization problem: where V is the training corpus, Φ(q k , d i ) is the feature vector of document d i with respect to query q k , s k,i is the relevance score of d i with respect to q k , w is a weight vector, C is the SVM cost parameter, and ξ i,j,k are slack variables.


Unfortunately, the above scheme for exploiting pairwise order information cannot be applied directly to the emotion ranking task, because the task requires us to rank emotions within a document rather than provide a ranking of documents.

the definitions of Φ , s k,i and s k,j do not apply to emotion ranking.


Let f i : E → ℜ be the emotional probability function of d i ∈D.

f i (e j ) outputs the fraction of readers who experience emotion e j after reading document d i .


The ordered list of emotions returned by Algorithm 1 at Line 7 is the final output of the PLM method.

we want to minimize the number of incorrectly-ordered emotion pairs.


After obtaining news articles, the next step is to determine how to convert them into feature vectors for SVM and SVR.


This stronger version of TREE requires further that the tree be projective.

it prohibits L ij and L k from both being true if i → j crosses k → .


The parser should determine the values of the individual variables.

we would like to marginalize equation 1to obtain the distribution p(L 34 ) over L 34 = true vs. false, the distribution p(T 4 ) over tags, etc.


We give a new theoretical motivation for stacking parsers, in terms of extending a parsing model's feature space.

we view stacked learning as a way of approximating non-local features in a linear model, rather than making empirically dubious independence (McDonald et al., 2005b) or structural assumptions (e.g., projectivity, Eisner, 1996), using search approximations (Sagae and Lavie, 2005;, solving a (generally NP-hard) integer linear program (Riedel and Clarke, 2006), or adding latent variables (Titov and Henderson, 2007).


Shallow methods like word or bigram overlap, (weighted) cosine or Jaccard similarity are appealing as they are cheap and robust.

nelken & Schieber (2006) demonstrate the efficacy of a sentence-based tf*idf score when applied to comparable corpora.


Entity grid features also make their way into the best models when more features are used for prediction: In this section we consider the problem of pairwise ranking of text readability.

rather than trying to predict the readability of a single document, we consider pairs of documents and predict which one is better.


The fragmentation step, however, becomes much more involved since we now face a choice of multiple parse hyperedges at each node.

it becomes nondeterministic how to "cut" a forest into tree fragments, which is analogous to the non-deterministic pattern-match in forest-based decoding (Mi et al., 2008).


However, a forest is an implicit collection of many more trees, each of which, when enumerated, has its own probability accumulated from of the parse hyperedges involved.

a forest can be viewed as a virtual weighted k-best list with a huge k. So a rule extracted from a non 1-best parse, i.e., using non 1-best hyperedges, should be penalized accordingly and should have a fractional count instead of a unit one, similar to the E-step in EM algorithms.


The pseudo-document O is an exponentially-weighted moving average of these vectors.

for each training sentence, letê be the 1-best translation; after processing the sentence, we update O, and its input length O f : We can then calculate the B score of hypotheses e in the context of O.


The uncertain-input model gives us a means of accounting for these results, because there are near neighbors of (1a) for which there is a global grammatical analysis in which either the deer or a coreferent NP is in fact the object of the subordinateclause verb hunted.

inserting the word it either before or after the deer creates such a near neighbor: (2) a.


There are some cases in which this is probably the correct answer: many regressive eye movements are very small and the consensus in the eye-movement literature is that they represent corrections for motor error at the saccadic level.

the eyes overshoot the intended target and regress to obtain in-formation about what was missed.


When w j happens no earlier than e i (i.e., d(e i , w j ) ≤ 0), the closer they are, the more likely they are aligned.

the temporal alignment probability of w j and e i in each cooccurring instance (w, e) is computed as where α is a constant for scaling d(e i , w j ).


Multiple entities of the same type have the same properties and share the same set of domain concepts.

we compute the semantic relatedness of a word w and an entity e based on the semantic similarity between w and the properties of e. semantic relatedness SR(e, w) is defined as "bed#n#1" "picture#n#2" "size#n#1" "color#n#1" "dresser#n#4"  where c i e is the i-th property of entity e, s(c i e ) is the synset of property c i e as designed in domain model, s j (w) is the j-th synset of word w as defined in wordNet, and sim(•, •) is the similarity score of two synsets.


Motivated by the psycholinguistic findings, we investigate the use of eye gaze for automatic word acquisition in multimodal conversational systems.

we investigate the use of speech-gaze temporal information and word-entity semantic relatedness to facilitate word acquisition.


This task replicates the word similarity task used in (Miller and Charles, 1991), following a previous  Figure 1: Non-expert correlation for affect recognition task initially proposed by (Rubenstein and Goodenough, 1965).

we ask for numeric judgments of word similarity for 30 word pairs on a scale of [0,10], allowing fractional responses 4 .


However, as we have already seen, the cross-document co-reference system does make mistakes and these mistakes can propagate to the within-document output.

we have noticed that the crossdocument system has a tendency to link person names with the same last name when both names appear in a single document.


Definition Head match  Another class of features captures the semantic relation between two words.

we check whether gender or number match, or whether the mentions are synonyms, antonyms, or hypernyms.


Our application is also atypical for an NLP application in that we use an approximate sampler not only to include Bayesian prior information (section 4), but also because computing phrase alignment expectations exactly is a #P-hard problem (DeNero and Klein, 2008).

we could not run EM exactly, even if we wanted maximum likelihood estimates.


8 This parameterization has an intuitive interpretation: under these parameters, the average of independent samples from the Dirichlet will converge to M 0 .

the average of the ith element of the samples will converge to µ i .


保险公司 (the insurance company) is labeled as ARG0, which is the proto-agent of the verb.

to the verb 提供 (provide), it is the provider.


保险服 务 (insurance services) is the direct object of the verb, and it is the proto-patient, which is labeled as ARG1.

to the verb 提供 (provide), it represents things provided.


Expectation-Maximization is a procedure that iteratively re-estimates the model parameters (θ, φ), converging on a local maximum of the likelihood.

if the parameter estimate at iteration is (θ ( ) , φ ( ) ), then the re-estimated parameters at iteration + 1 are: (2) where n w,t is the number of times word w occurs with state t, n t ,t is the number of times state t follows t and n t is the number of occurences of state t; all expectations are taken with respect to the model The experiments below used the Forward-Backward algorithm (Jelinek, 1997), which is a dynamic programming algorithm for calculating the likelihood and the expectations in (2) in O(nm 2 ) time, where n is the number of words in the training corpus and m is the number of HMM states.


The common approach adopted is therefore to view this problem as a classification problem (Klementiev and Roth, 2006a;Tao et al., 2006) and train a discriminative classifier.

given two strings, one in the source and the other in the target language, extract pairwise features, and train a classifier that determines if one is a transliteration of the other.


The trend across all three conferences is clear: each conference is not only increasing in breadth, but also in similarity.

eMNLP and ACL's differences, once significant, are nearly erased.


Since the implicit features it uses are dependency paths which are enumerable, it is a well defined kernel.

an example gets implicitly mapped to the feature space in which each dependency path is a dimension.


Usually the IBM Model 1, developed in the statistical machine translation field (Brown et al., 1993), is used to construct translation models for retrieval purposes in practice.

given a number of parallel strings, the IBM Model 1 learns the translation probability from a source word s to a target word t as: where λ s is a normalization factor to make the sum of translation probabilities for the word s equal to 1, N is the number of parallel string pairs, and J i is the ith parallel string pair.


As an alternative term weighting approach, we have used a variant of Mihalcea and Tarau (2004)'s Tex-tRank, a graph-based ranking model for keyword extraction which achieves state-of-the-art accuracy without the need of deep linguistic knowledge or domain-specific corpora.

the ranking algorithm proceeds as follows.


Taking inspiration from higher-order functions in functional programming, MapReduce provides an abstraction at the point of these two operations.

the programmer defines a "mapper" and a "reducer" with the following signatures: Key-value pairs form the basic data structure in MapReduce.


In addition, they were also asked to assign a polarity to each feature.

the polarity tells us whether or not the features belong to the positive class.


This approach appears straightforward, but it must resolve two practical issues.

first, the task of the classifier is not only to make a binary decision for the two strings s and t, but also to enumerate a set of positive strings for the string s, an efficient algorithm is necessary to find a set of strings with which the classifier predict(s, t) yields positive labels for the string s. Another issue arises when we prepare a training set.


We define c s as the substring in s that is not covered by the longest common prefix l and postfix r, and define c t for t analogously.

strings s and t are divided into three regions, lc s r and lc t r, respectively.


Combined : the combined method presented in Section 5.

it uses WNet-full as a default and Dist-word as back-off.


Results show that all our models largely outperform both baselines, achieving a good level of accuracy and high coverage.

accuracy for the best-10 frames is high enoungh to support tasks such as the semi-automatic creation of new FrameNets.


The vectorial representation of LUs with few occurrences in the corpus is likely to be semantically incomplete, as not enough statistical evidence is available.

skewed distributions can be found when some frames are very rarely represented in the corpus.


(1) Finding them is really a pain, you know (2) The company's decided that teletext is outdated Subjective questions are questions in which the speaker is eliciting the private state of someone else.

the speaker is asking about what someone else thinks, feels, wants, likes, etc., and the speaker is expecting a response in which the other person expresses what he or she thinks, feels, wants, or likes.


During the process of IMT for a given source sentence, the system makes use of the word graph generated for that sentence in order to complete the prefixes accepted by the human translator.

para encender la impresora: REFERENCE (y): To power on the printer: To switch on: power on the printer: To power on the printer: To power on the printer: : Example of non-explicit positioning MA which solves an error of a missing word.


So we expand the idea of incremental parsing by introducing greedy search.

we look for the hypotheses over the cell [1, n] by building analyses over where j < i, k < i and a j,2 + 1 = a k,1 .


We will apply this algorithm to dependency parsing of Lexicalized Tree Adjoining Grammar (Joshi and Schabes, 1997).

we will train and evaluate an LTAG dependency parser over the LTAG treebank described in Shen et al.


Therefore, if the goal is to accurately learn and pre- 3 Hypertext Topic Model In topic modeling, a probability distribution of words is employed for a given document.

the probability distribution is defined as a mixture over latent topics, while each topic is future characterized by a distribution of words (Hofmann, 1999;Blei et al., 2003).


The generative process of LDA has three steps.

in each document a topic distribution is sampled from a prior distribution defined as Dirichlet distribution.


We applied the four models in the three datasets to document classification.

we used the word distributions of documents created by the models as feature vectors of the documents and used the subjects in the datasets as categories.


In contrast, HTM can accurately learn topics by the help of the cited documents.

if the content of a document is diverse, then words in the document are likely to be assigned into wrong topics by the existing approaches.


We award points as a motivating factor, but this can lead players to enter irrelevant data, or collude with other players, in order to get a higher score.

collusion is more likely when players can freely communicate.


In the context of automatic text summarisation, we examine the problem of statistical novel sentence generation, with the aim of moving from the current state-of-the-art of sentence extraction to abstractlike summaries.

we focus on the task of selecting content to include within a generated sentence.


We propose and evaluate a method called "Seed and Grow" for selecting content from auxiliary sentences.

we first select the core meaning of the summary, given here by the key sentence, and then we find those pieces of additional information that are conventionally juxtaposed with it.


This paper seeks to determine whether or not such patterns exist in homogeneous data; and furthermore, whether such patterns can be used to better select words from auxiliary sentences.

we propose the "Seed and Grow" approach for this task.


However, the resources for other languages are limited.

few reliable resources are available for Chinese sentiment analysis 1 and it is not a trivial task to manually label reliable Chinese sentiment resources.


This is possible because both models are global and linear.

the transition-based model can be written as: If we take T ∈act(y) Φ(T , s T ) as the global feature vector Φ T (y), we have: which has the same form as the graph-based model:  We therefore combine the two models to give: Concatenating the feature vectors Φ G (y) and Φ T (y) to give a global feature vector Φ C (y), and the weight vectors w G and w T to give a weight vector w C , the combined model can be written as: which is a linear model with exactly the same form as both sub-models, and can be trained with the perceptron algorithm in Figure 1.


This can be explained by the difference between the decoders.

the graph-based model is unable to score the actions "Reduce" and "Shift", since they do not modify the parse tree.


Each instance-class pair (i, C) extracted in the first phase (Section 2) is represented as a weighted edge in a graph G = (V, E, W ), where V is the set of nodes, E is the set of edges and W : E → R + is the weight function which assigns positive weight to each edge.

for each (i, C, w) triple from the set of base extractions, i and C are added to V and (i, C) is added to E, 2 with W (i, C) = w. The weight w represents the total score of all extractions with that instance and class.


In what follows, all nodes are treated in the same way, regardless of whether they represent instances or classes.


We are given a set of instances I and a set of classes C represented as nodes in the graph, with connecting edges as described above.

we annotate a few instance nodes with labels drawn from C. classes are used both as nodes in the graph and as labels for nodes.


We propose novel approaches for two sub-tasks of the problem: identifying the entities of interest, and partitioning and describing the relations based on their semantics.

we show that term frequency patterns can be used effectively instead of supervised NER, and that the pmedian clustering objective function naturally uncovers relation exemplars appropriate for describing the partitioning.


To discover them, we use a slightly modified version of the method presented in (Davidov and Rappoport, 2006).

in this work we consider named entities of arbitrary length (i.e., longer than a single token).


We further assume that for a relationship to be useful, a number of different entities should stand in this relation.

we inspect the set of left and right arguments in the cluster, which (in English) usually correspond to the subject and object of the sentence.


In this section we test several hypotheses of RD using data sets which are already labeled for sentences which contain entities of a particular type and in a fixed relation of some kind.

we adapt the output of the discovery phase to identify phrases which express PPIs.


RD-F 1 reports parameters which give highest recall and RD-P highest precision.

both RD-F 1 and RD-P use a minimum RP length of 1, RD-F 1 uses a co-occurrence threshold of 10, and RD-P uses a co-occurrence threshold of 50.


Experimenting with this parameter reveals that α = −0.9, β = 1 yields a correlation of 78.9%.

a metric that unboundedly rewards spuriously inserted words correlates better with human judgments than a metric that punishes them.


, w |N | } be the set of n-grams and let a local gain function g w : E × E → R, for w ∈ N , be as follows: where θ w is a constant, # w (E ) is the number of times that w occurs in E , and δ w (E) is 1 if w ∈ E and 0 otherwise.

g w is θ w times the number of occurrences of w in E , or zero if w does not occur in E. We first assume that the overall gain function G(E, E ) can then be written as a sum of local 1 using the log semiring's extend operator gain functions and a constant θ 0 times the length of the hypothesis E .


Our experiments show that the main improvement comes from the larger evidence space: a larger set of translations in the lattice provides a better estimate of the expected BLEU score.

the lattice provides a better posterior distribution over translation hypotheses relative to an Nbest list.


While we have applied lattice MBR decoding to the approximate BLEU score, we note that our procedure (Section 3) is applicable to other gain functions which can be decomposed as a sum of local gain functions.

our framework might be useful with transla-tion metrics such as TER (Snover et al., 2006) or METEOR (Lavie and Agarwal, 2007).


Huang et al., 2008)), a binarizable segmentation/permutation can be recognized by a binarized Synchronous Context-Free Grammar (SCFG), i.e., an SCFG in which the right hand sides of all non-lexical rules constitute binarizable permutations.

this holds for the SCFG implementing Inversion Transduction Grammar (Wu, 1997).


A valid clustering is a clustering in which the relevant entries satisfy the transitivity constraint.

c is valid if and only if ( Hence, a valid clustering corresponds to a partition of a given set of mentions, and the goal of coreference resolution is to produce a valid clustering in which each cluster corresponds to a distinct entity.


To reduce data sparseness and improve the estimation of the above probabilities, we make conditional independence assumptions about the generation of these feature values.

as shown in the first column of Table 1, we divide the seven features into three groups (namely, strong coreference indicators, linguistic constraints, and mention types), assuming that two feature values are conditionally independent if and only if the corresponding features belong to different groups.


To induce a clustering C on a document D, we run EM on our model, treating D as observed data and C as hidden data.

we use EM to iteratively estimate the model parameters, Θ, from documents that are probabilistically labeled (with clusterings) and apply the resulting model to probabilistically re-label a document (with clusterings).


It uses data structures S and the H i 's to store intermediate results.

s(P P ) stores the score of the partial partition P P .


When processing m i , it takes each partial partition P P in H i−1 and creates a set of ith-order partitions by extending P P with m i in all possible ways.

for each cluster C (formed by a subset of the first i-1 mentions) in P P , the algorithm generates a new ith-order partition, P P ′ , by linking m i to C (line 9), and stores P P ′ in H i (line 11).


Given a document, the goal is to find the most probable assignment of entity indices to its mentions given the observed values.


In a Bayesian approach, we compute this probability by integrating out all the parameters.


Before introducing this pronoun head model, we need to augment the set of entity-specific parameters, which currently contains only a distribution over heads (φ h Z ).

we add distributions φ t Z , φ g Z , and φ n Z over entity properties: φ t Z is a distribution over semantic types (PER, ORG, LOC, MISC), φ g Z over gender (MALE, FEMALE, EITHER, NEUTER), and φ n Z over number (SG, PL).


Note that this modification is applied only to the basic model.

pronoun generation continues to be handled by the pronoun head model and will not be affected.


To eliminate this possibility, we enforce the agreement constraints at the global level.

we sample an entity index for a given mention with a non-zero probability if and only if the corresponding entity and the head of the mention agree in gender and number.


The entity-based CEAF scorer was proposed in response to these two weaknesses.

it operates by computing the optimal alignment between the set of reference entities and the set of system entities.


We use a small amount of labeled data for parameter initialization for the two models.

for evaluations on the BNEWS test data, we use as labeled data one randomly-chosen document from the BNEWS training set, which has 58 true mentions and 102 system mentions.


Our second baseline is obtained by running only one iteration of our EM-based coreference model.

it starts with the M-step by initializing the model parameters using the labeled document, and ends with the E-step by applying the resulting model (in combination with the Bell tree search algorithm) to obtain the most probable coreference partition for each test document.


We do not assume any other labeled information.

we do not assume gold name entity recognition (NER) labels, and unlike Haghighi & Klein (2007), we do not assume gold mention types (for ACE datasets, they also used gold head words).


In addition to being conceptually simple, these modifications of the standard single-model, classification-based approach also deliver significant performance improvements.

we show that on the ACE corpus both strategies produce f-score gains of more than 3% across the three coreference evaluation metrics (MUC, B 3 , and CEAF).


Both these strategies lead to improvements for all three standard coreference metrics: MUC (Vilain et al., 1995), B 3 (Bagga and Baldwin, 1998), and CEAF (Luo, 2005).

our specialized ranker system provides absolute f -score improvements against an otherwise identical standard classifier system by 3.2%, 3.1%, and 3.6% for MUC, B 3 , and CEAF, respectively.


Basically, the idea is that linguistic form is an indicator of the status of the corresponding referent in the discourse model.

the use by the speaker of a particular linguistic form corresponds to a particular level of activation (or familiarity or salience or accessibility) in (what she thinks is) the addressee's discourse model.


The first oracle system, RANK+DS-ORACLE+SP, uses the specialized rankers in combination with a perfect discourse status classifier.

this system knows for each mention whether it is anaphoric or not: the only errors made by such a system are invalid resolutions.


The second oracle system, LINK-ORACLE, uses the discourse status classifier with a perfect coreference resolver.

this system has perfect knowledge regarding the antecedents of anaphors: the errors made by such a system are only errors in the discourse status of mentions.


The question is: given this additional source of knowledge (i.e., χ), has the learning problem become easier?

can we learn f 2 (and/or f 1 ) using significantly fewer labeled examples than if we did not have χ?


Our goal is to prove that one-sided learning with hints "works."

if C 2 is learnable from large amounts of labeled data, then it is also learnable from small amounts of labeled data and large amounts of f 1 -labeled data.


We further compare the algorithms by looking at how many training setting has each as the winner.

we compare both hints and self-training to the two baselines, and then compare hints to selftraining.


This is not surprising: the form of the constraint function we use tells us a lot about the NER task, but relatively little about the syntactic chunking task.

it tells us nothing about phrases other than NPs.


However, feature similarity -possibly measured using unlabeled data -could be used to weigh source domains.

we combine the parameters of each classifier according to their confidence using the combination methods described above.


Evgeniou and Pontil (2004) achieved a similar goal by using shared parameters for multi-task regularization.

they assumed that the weight vector for problem d could be represented as w c = w d +w s , where w d are task specific parameters and w s are shared across all tasks.


It states that one relation must be set to 1 and the rest to 0.

a pair of events cannot have two relations at the same time.


Rather than include all unlabeled pairs in our unknown set, we only include the unlabeled pairs that span at most one sentence boundary.

events in adjacent sentences are included in the unknown set if they were not tagged by the Timebank annotators.


While past situations have already happened by definition, future situations by nature are characterized by uncertainty.

future situations may or may not happen.


In a theoretic work on the temporal interpretation of verbs in languages like Chinese which lacks tense morphology, Smith and Erbaugh (2005) pointed out that there is a default interpretation for bounded and unbounded situations.

bounded situations are temporally located in the past by default while unbounded situations are located in the future.


At first glance, the problem may seem trivial, yet the presence and position of these function words can have crucial impact on the adequacy and fluency of translation.

function words are often used to denote key semantic information.


We can expand the features analysis by considering the effectiveness of certain subclasses of features.

we group the summarization features into lexical, structural, participant and length features.


This creates a challenging, hidden-variable learning problem that we address with the use of an insideoutside algorithm.

we develop a dynamic programming parsing algorithm that leads to O(n 3 m) time complexity for inference, where n is the sentence length and m is the size of meaning structure.


This pipelined approach achieves state-ofthe-art performance on two publicly available corpora.

the flexible generative model leads to notable improvements in recall, the total percentage of sentences that are correctly parsed.


With different context assumptions, we reach different variations of the model.

we consider three assumptions, as follows: Model I We make the following assumption: (3) where t k is a semantic category or a NL word, and m j is an MR production.


In particular, we consider three assumptions, as follows: Model I We make the following assumption: (3) where t k is a semantic category or a NL word, and m j is an MR production.

generation of the next NL word depends on its direct parent MR production only.


Model II We make the following assumption: where t k−1 is the semantic category or NL word to the left of t k , i.e., the previous semantic category or NL word.

generation of the next NL word depends on its direct parent MR production as well as the previously generated NL word or semantic category only.


In a conventional PCFG parsing task, during the training phase, the correct correspondence between NL words and syntactic structures is fully accessible.

there is a single deterministic derivation associated with each training instance.


Determining the polarity of a sentimentbearing expression requires more than a simple bag-of-words approach.

words or constituents within the expression can interact with each other to yield a particular overall polarity.


VOTE is defined as the majority polarity vote by words in a given expression.

we count the number of positive polarity words and negative polarity words in a given expression, and assign the majority polarity to the expression.


The polarity lexicon is initialized with the lexicon of  and then expanded using the General Inquirer dictionary.

6 a word contained in at least two of the following categories is considered as positive: POSITIV, PSTV, POSAFF, PLEASUR, VIRTUE, INCREAS, and a word contained in at least one of the following categories is considered as negative: NEGATIV, NGTV, NEGAFF, PAIN, VICE, HOSTILE, FAIL, ENLLOSS, WLBLOSS, TRAN-LOSS.


Once we determine the intermediate decision variables, we apply the heuristic rules motivated by compositional semantics (from Table 2) in order to obtain the final polarity y of x.

y = C(x, z), where C is the function that applies the compositional inference, either COMPOPR or COMPOMC.


Instead, we propose a simple and tractable training rule based on the creation of a soft gold standard for z.

we exploit the fact that in our task, we can automatically construct a reasonably accurate gold standard for z, denoted as z * : as shown in Figure 2, we simply rely on the negator and polarity lexicons.


However, their notion of polarity is quite different from that assumed here and in the literature on sentiment analysis.

it refers to the degree of "commitment" of the author to the truth or falsity of a complement clause for a textual entailment task.


We take "English-like" to mean most probable according to some statistical language model, whose job is to assign some probability to any sequence of letters.

according to a 1gram model of English, the probability of a plaintext p 1 ...p n is given by: we obtain the probability of a sequence by multiplying together the probabilities of the individual letters that make it up.


If variable key aQ = 1, that means the key maps plaintext a to ciphertext Q.

clearly, not all assignments to these 729 variables represent valid keys, so we augment the "subject to" part of our integer program by requiring that for any letter x, subject to: every plaintext letter must map to exactly one ciphertext letter, and every ciphertext letter must map to exactly one plaintext letter.


Suppose node A represents the decipherment of ciphertext letter c i as plaintext letter p j -for all such nodes, we stipulate that the sum of values for link (i−1)xp j (for all x) equals the value of key p j c i .

whether a node lies along the chosen decipherment path or not, the chosen key must support that decision.


Before even seeing C, we can compute our uncertainty about the key K by noting that there are 26!

equiprobable keys: 4 any secret key can be revealed in 89 bits.


Given a group of WFSTs, each of which models a stage of a mapping cascade, the composition operation provides an efficient approach to combine them into a single one (Mohri et al., 2002;Mohri et al., 1996).

for two WFSTs R and S, the composition T = R o S represents the composition of relations realized by R and S. The combination is performed strictly on R's output and S's input.


Following (Hsu and Glass, 2006), we also consider features derived from the HMM-LDA word topic labels.


In Table 3, we compare the performance of n-gram weighting with the h doc document entropy feature for various modified Kneser-Ney smoothing configurations (Chen and Goodman, 1998) on the Lectures dataset.

we considered varying the number of discount parameters per n-gram order from 1 to 10.


Thus, during feature selection for speech recognition applications, we should consider the development set WER.

since the differences in WER are often statistically insignificant, we will select the feature that minimizes the sum of the development set WER and log perplexity, or cross-entropy.


Is there a assignment β : X → {0, 1} such that β |= F?

if we replace each x in F by β(x), and each x by 1 − β(x), does F become true?


In the example in Figure 5, the three leftmost column heads correspond to these nodes.

create a unique node for each "running" word in R -e.g.


1 The probability is conditioned on both a i−1 and a i to make sure that the label o i is consistent with the phrase alignment.

probabilities in these models can be greater than zero only if one of the following conditions is true: At decoding time, rather than using the logprobability of Eq.


Word-based orientation model: This model analyzes word alignments at positions (s − 1, u − 1) and (s − 1, v + 1) in the alignment grid shown in Phrase-based orientation model: The model presented in (Tillman, 2004) is similar to the wordbased orientation model presented above, except that it analyzes adjacent phrases rather than specific word alignments to determine orientations.

orientation is set to o i = M if an adjacent phrase pair lies at (s − 1, u − 1) in the alignment grid.


Hierarchical orientation model: This model analyzes alignments beyond adjacent phrases.

orientation is set to o i = M if the phraseextract algorithm is able to extract a phrase pair at (s − 1, u − 1) given no constraint on maximum phrase length.


In recent years, latent variable methods have been shown to produce grammars which are as good as, or even better than, earlier parsing work (Collins, 1999;Charniak, 2000).


Since a multi-scale grammar has productions which can refer to different levels of the category hierarchy, there must be constraints on their coherence.

for each fully refined production, exactly one of its dominating coarse productions must be in the grammar.


We also look at the possibility of automatically detecting certain "cue" words that are appropriate for our prediction problem.

we want to see if we can detect certain words that have a discriminating power in deciding whether parsing a sentence that contains them is difficult or easy.


The algorithm minimizes the following cost function: where e z is the error for a target node z defined as the squared difference between the final score assigned to z by the graph walk, p z , and some ideal score according to the example's labels, p Opt z .

4 p Opt z is set to 1 in case that the node z is relevant or 0 otherwise.


We evaluate the text representation schema and the proposed set of graph-based similarity measures on the task of coordinate term extraction.

we evaluate the extraction of named entities, including city names and person names from newswire data, using word similarity measures.


However, noisy negative examples were considered for the larger automatically annotated AP corpus.

(for cities, we only considered city names included in the MUC corpus as correct answers.)


Both of these learning methods utilize a richer set of features than the graph walk and weight tuning, which can consider only local information.

while the graph walk paradigm assigns lower importance to longer connecting paths (as described in Section 3), reranking and the path-constrained walker allow to discard short yet irrelevant paths, and by that eliminate noise at the top ranks of the retrieved list.


First, the representation must be directly derivable from the perceived input.

it cannot rely on the results of any disambiguation that would occur either as part of exemplar-theoretic perception or in further downstream processing.


We refer to the representations of left and right contexts of a given word as half-words.

we split a word into two entities, a left halfword that characterizes its behavior to the left and a right half-word that characterizes its behavior to the right.


We define the Denote by N the diagonal The conditional probability p left of the following word and the conditional probability p right of the preceding word can be computed by multiplying (the transpose of) J and N : The "grounding" paradigmatic distributions of order 1 are defined as follows.

each word has only one perfect left / right substitute and that perfect substitute is itself.


In contrast to Li and Roth (2002)'s approach which makes use of very rich feature space, we propose a compact yet effective feature set.

we propose head word feature and present two approaches to augment semantic features of such head words using WordNet.


In contrast to Li and Roth (2006)'s approach which makes use of a very rich feature set, we propose to use a compact yet effective feature set.

we propose head word feature and 1 available at http://12r.cs.uiuc.edu/∼cogcomp/Data/QA/QC present two approaches to augment semantic features of such head words using WordNet.


To better cover the question sentences, we further re-define the semantic head finder rules to fit our needs.

the rules to find the semantic head word of phrases SBARQ (Clause introduced by subordinating conjunction), SQ (subconstituent of SBARQ excluding wh-word or whphrase), VP (verb phrase) and SINV (declarative sentence with subject-aux inversion) are redefined, with the head preference of noun or noun phrase rather than verb or verb phrase.


If the head word is any of name, type or kind etc, post fix is required to identify the real head word if necessary.

we compile a tree pattern as shown in the left of Figure 3.


In this approach, we make use of the WordNet Similarity package (Seco et al., 2004), which implicitly employs WordNet hypernyms.

for a given pair of words, the WordNet similarity package models the length of path traveling from one word to the other over the WordNet network.


The first experiment evaluates the individual contribution of different feature types to question classification accuracy.

the SVM and ME are trained from the UIUC 5500 training data using the following feature sets: 1) whword + head word, 2) wh-word + head word + direct hypernym, 3) wh-wod + head word + indirect hypernym, 4) unigram, 5) bigram, 6) trigram, and 7) word shape.


In contrast to Li and Roth (2006)'s approach which makes use of very rich feature space, we proposed a compact yet effective feature set.

we proposed head word feature and presented two approaches to augment semantic features of such head words using WordNet.


Finally, learning how to identify question orientation is a crucial component of inferring user intent, a long-standing problem in web information access settings.

we focus on the following research questions: • Can we utilize the inherent structure of the CQA interactions and use the unlimited amounts of unlabeled data to improve classification performance, and/or reduce the amount of manual labeling required?


Intuitively, we can consider the text of the question itself or answers to it.

we have multiple (at least two) natural views of the data, which satisfies the conditions of the co-training approach (Blum and Mitchell, 1998).


The batching of questions was done to easily detect the "random" ratings produced by irresponsible workers.

each worker rated a batch of 25 questions.


For the subjectivied experiments to follow, we attempt to capture the linguistic characteristics identified in previous work (Section 5) in a lightweight and robust manner, due to the informal and noisy nature of CQA.

we use the following feature classes, computed separately over question and answer content: Character Word and POS n-gram (n<=3, i.e.


The rows show performance considering only the question text (question), the best answer (best_ans), text of all answers to a question (all_ans), the text of the question and the best answer (q_bestans), and the text of the question with all answers (q_allans), respectively.

using the words in the question alone achieves F1 of 0.717, compared to using words in the question and the best answers text (F1 of 0.695).


While a validation set should have been used for CoCQA parameter tuning, Figures 3 and 4 indicate that CoCQA is not sensitive to the specific parameter settings.

we observe that any K is greater than 100, and for any number of iterations R is greater than 10, CoCQA exhibits in effectively equivalent performance.


Surprisingly, CoCQA exhibits comparable performance of F1=0.685 with only 200 labeled examples are used, compared to the F1=0.695 for Supervised with all 800 labeled training examples on this feature set.

cocQA is able to achieve comparable performance to supervised SVM with only one quarter of the labeled training data.


Preliminary experiments showed that we can obtain a good balance between the amount and quality of the documents fetched by using only rare question terms as hint words.

we select the three question words that occur least frequently in a sample of the AQUAINT corpus as hints.


This section describes machine-learning approaches for inferring the task-specific information modeled by the form-based dialog structure representation from human-human conversations.

the learning approach has to infer a list of tasks, sub-tasks and concepts in a given domain from in-domain dialogs similar to what a human does in Section 3.


For example, the word "delta" which occurs many times in a re-serve_flight subtask but does not occur in other subtasks is undoubtedly useful for determining subtask boundaries while the word "you" which can occur anywhere in a dialog is not useful.

a regularity count of word w is defined as the number of sliding context windows in the similarity score calculation of TextTiling that contain the word w in each dialog.


Some human supervision can be valuable particularly in the form of semisupervised learning and active learning.

a process that integrates human input at appropriate times (for example seeding or correction) is likely to be part of a successful approach.


In Section 5 we illustrate the usefulness of our metric by analyzing a set of conversations in various ways using the RRD.

in that section we will empirically demonstrate its robustness to noise and data sparseness compared to the popular term frequency and TF-IDF cosine distance approaches in a dialog classification task.


The superscript u denotes that this ranked list is based on the universal language.

the occurrence in the language denoted by for every j where j i < (for notational simplicity we assume that no two words share the same frequency).


Verify if the rank of the feature needs to be updated in the segment rank list.

evaluate whether still holds true after the update.


The chosen call, specifically shows as topic AIR-BAG deployment in the context of a car accident.

figure 2 shows the corresponding rank in the universal ranked dictionary versus the rank in the dialog or segment.


The performance measures shown on Tables 2 and  3 show that NB outperforms VFI in most of the configurations tested.

nB yields the best results when using a 1 word context with no lexical forms nor lemmas as attributes (see Table 2 row 3).


For the randomly generated CS sentences, switching decisions are made randomly with a probability proportional to the positive predictions made by the classifiers (in this case NB).

for the Spanish sentences switch points are predicted randomly with a 30% chance of switching while for English switch points are predicted with a 10% chance.


Using the distributional hypothesis of antonyms, we claim that the degree of antonymy between two contrasting concepts (thesaurus categories) is directly proportional to the distributional closeness of the two concepts.

the more the words representing two contrasting concepts occur in similar contexts, the more the two concepts are considered to be antonymous.


The corpus is designed for the idiom token identification task.

each example sentence in the corpus is annotated with a label that indicates 6 Some idioms like by and large do not have a literal meaning.


In the rest of this paper, we perform domain adaptation experiments for WSD, focusing on domain adaptation methods that use in-domain annotated data.

we use a feature augmentation technique recently introduced by Daume III (2007), and active learning (Lewis and Gale, 1994) to perform domain adaptation of WSD systems.


In this modified graph, if an instance co-occurs with a pattern which also co-occurs with a large number of other instances, a self-loop of a node in the instance similarity graph induced by M T M will receive a higher negative weight.

instances co-occurring with generic patterns will get less weight in the regularized Laplacian than in the von Neumann kernels.


We also conduct preliminary investigative experiments into the practical applicability of leveraging query-trained part-of-speech taggers for information-retrieval tasks.

we show that part-of-speech information can be a significant feature in machine-learned searchresult relevance.


But we would like to understand the use of capitalization in queries with varied case.

how frequently does first-letter capitalization indicate a proper noun?


It achieves this accuracy by expanding information sources for tagging.

it provides "(i) more extensive treatment of capitalization for unknown words; (ii) features for the disambiguation of the tense forms of verbs; (iii) features for disambiguating particles from prepositions and adverbs."


We present a novel method for discovering and modeling the relationship between informal Chinese expressions (including colloquialisms and instant-messaging slang) and their formal equivalents.

we proposed a bootstrapping procedure to identify a list of candidate informal phrases in web corpora.


Informal text tends to have very different style from formal text (e.g., newswire, magazine, etc.).

they are different in vocabulary, syntactic structure, semantic interpretation, discourse  structure, and so on.


In this paper, we present a novel method for discovering and modeling the relationship between informal Chinese expressions found in web corpora and their formal equivalents.

we implement a bootstrapping procedure to identify a list of candidate informal phrases.


Here, we use the word "homophone" in a loose way.

we refer an informal phrase as a homophone of a formal phrase if its pronunciation is the same or similar to the formal phrase.


In the three examples belonging to the homophone category in  For illustrative purposes, we can present the transformation path showing how the informal phrase is obtained from the formal phrase.

the transformation path for this category is "Formal → PinYin → Informal (similar or same PinYin as the formal phrase)".


Text 2 9 ( ) 20 10 8 In this section, we describe an approach that automatically discovers the relation between a formal phrase and an informal phrase from web corpora.

we propose a bootstrapping procedure to identify a list of candidate informal phrases.


Alternatively, one can employ a large amount of formal text (e.g., newswire) and informal text (e.g., Internet blogs) to derive such a list as follows.

from the informal corpus we can extract those phrases whose frequency in the informal corpus is significantly different from that in the formal corpus.


An alternative approach to extracting the informal phrases is to use a bootstrapping algorithm (e.g., Yarowsky (1995)).

we first manually collect a small set of example relations.


Given an informal phrase, we retrieve training data from the web on the fly.

we first use a search engine to identify a set of hyper-links that point to web pages containing contexts relevant to the informal phrase, and then follow the hyper-links to download the web pages.


In this section, we use a subset of them for training and testing.

we use 252 examples to train the log-linear model that is described in Section 4, and use 249 examples as test data to compute the precision.


We also carried out experiments (see Table 10) in the bootstrapping procedure described in Section 4.1.

we start from a seed set having 130 relations.


Recently, Chang and Lai (2004), Lee (2005), Chang and Teng (2006), Li and Yarowsky (2008) have investigated this task.

chang and Lai (2004) describes a hidden markov model (HMM) to model the relationship between a full-form phrase and its abbreviation, by treating the abbreviation as the observation and the fullform words as states in the model.


We have then proposed a novel method for discovering and modeling the relationship between informal Chinese expressions (including colloquialisms and instant-messaging slang) and their formal equivalents.

we have proposed a bootstrapping procedure to identify a list of candidate informal phrases in web corpora.


, θ (C) } to represent C different hypotheses that are consistent with the labeled set L. The most informative query, then, is the instance over which the committee is in most disagreement about how to label.

we use the query-by-bagging approach (Abe and Mamitsuka, 1998) to learn a committee of CRFs.


We argue that this phenomenon can occur with sequence labeling tasks as well as with classification.

to address this, we propose a new active learning approach called information density (ID): the informativeness of x is weighted by its average similarity to all other sequences in U, subject to a parameter β that controls the relative importance of the density term.


To do this, we first transform each x, which is a sequence of feature vectors (tokens), into a single kernel vector x: where f j (x t ) is the value of feature f j for token x t , and J is the number of features in the input representation 2 .

sequence x is compressed into a fixed-length feature vector x, for which each element is the sum of the corresponding feature's values across all tokens.


In this paper, we have presented a detailed analysis of active learning for sequence labeling tasks.

we have described and criticized the query selection strategies used with probabilistic se- quence models to date, and proposed several novel strategies to address some of their shortcomings.


A recurring problem in computational linguistics and language processing is transduction of character strings, e.g., words.

one wishes to model some systematic mapping from an input string x to an output string y.


To obtain the numerator of equation 1, with its A∈Axy , we sum over all paths in U θ that are compatible with x and y.

we build x • π −1 x • U θ • π y • y and sum over all paths.


Here one assumes that the data (both labeled and unlabeled) is embedded within a low-dimensional manifold expressed by a graph.

each data sample is represented by a vertex within a weighted graph with the weights providing a measure of similarity between vertices.


We consider the transductive learning problem, i.e., given a training set D = {D l , D u }, where D l and D u are the sets of labeled and unlabeled samples respectively, the task is to infer the labels for the samples in D u .


More importantly, it separates the complexity in syntactic analysis from the semantic one, and makes the latter much easier to perform.

meaning composition does not require domain-specific procedures for generating candidate lexicons, as is often needed by supervised methods.


This allows the maximum flexibility in learning.

lexical entries are no longer limited to be adjacent words as in Zettlemoyer and Collins (2005), but can be arbitrary fragments in a dependency tree.


Grouping is done in such a way as to encourage the intended mixture behaviors.

for the rule p ∈ +c ∧ Form(p, +f), all instances given a fixed c form a group; for each of the remaining three rules, all instances given a fixed a form a group.


Specifically, for the rule p ∈ +c ∧ Form(p, +f), all instances given a fixed c form a group; for each of the remaining three rules, all instances given a fixed a form a group.

notice that with these constraints the completion likelihood P (Q, L) can be computed in closed form for any L. each formula group contributes a term equal to the weight of the currently satisfied formula.


We solve both problems jointly, using a graph alignment algorithm.

we view the task of inferring annotations for new verbs as an instance of a structural matching problem and follow a graph-based formulation for pairwise global network alignment (Klau, 2009).


Our implementation followed closely the model presented in Fürstenau and Lapata (2009) as it was used in a similar task and obtained good results.

we used a context window of five words on either side of the target word, and 2,000 vector dimensions.


the one-versus-one strategy for multi-class classification (Friedman, 1996).

the labeler was trained on the train seen data set without any access to training instances representative of the "unknown" verbs in test unseen.


The proof for the second-order expectation semiring is similar.

one mainly needs to show that In Tables 1-2, we do not discuss how to store p, r, s, and t. If p is a probability, it often suffers from the underflow problem.


Therefore, we represent real numbers as ordered pairs.

to represent a = s a e a , we store s a , a , where the s a ∈ {+, −} is the sign bit of a and the floatingpoint number a is the natural logarithm of |a|.


It turns out that these semirings can also compute first-and second-order partial derivatives of all the above results, with respect to a parameter vector θ ∈ R m .

we ask how they are affected when θ changes slightly from its current value.


To achieve competitive performance, we adopt a forest reranking approach (Li and Khudanpur, 2009;Huang, 2008).


(2006) point out that it is not always appropriate to use BLEU to compare systems to each other.

the quality of rule-based systems is usually underestimated by BLEU.


The first assumption we make to minimize the number of human judgments, is that once we have a judgment for a source-candidate substring pair, that same judgment can be used across all candidates for this source sentence.

we build a database for each source sentence, which consists of <source substring,target substring,judgment> entries.


And so the alignment approach we suggest here can be applied to a different MT system as well, as long as that system provides phrasal alignment along with the output.

a phrase-based system such as Moses can be modified in a straightforward manner to provide phrasal alignments, and then apply our method.


To this end, we collected a complete set of judgments for 50 source sentences and their candidates.

we generated a query for each and every node   Table 1.


In this paper, we attempt to bring some clarity to the situation by taking a closer look at one of these existing methods.

we cast the popular technique of cube pruning (Chiang, 2007) in the well-understood terms of heuristic search (Pearl, 1984).


1 To flatten the notation, we will generally represent items as a 4-tuple, e.g.

in CKY, new items are created by applying rules to existing items: (5) we are allowed to apply a rule r to a pair of items ι 1 , ι 2 if the item spans are complementary and preconditions(r) = postcondition(ι 1 ), postcondition(ι 2 ) .


Here we need to assume that our A* protocol differs slightly from standard A*.

it will practice node-tying, meaning that when it visits a rule node or an item node, then it also (atomically) visits all nodes on the path to its lookahead goal node.


In a given span, many boundary word patterns are repeated.

for a particular span [α, β] and carry κ, we often see many items of the form [α, β, X, κ], where the only difference is the postcondition X.


Most rules do not introduce lexical items.

most of the grammar rules have the form Z The idea is simple.


The fourth feature exploits structural information on the source side.

the decoder simultaneously generates both the source and target side dependency trees, and employs two dependency LMs, one for the source and the other for the target, for scoring translation hypotheses.


In principle, one could use a 100-tape WFSM to jointly model the 100 distinct forms of a typical Polish verb.

the WFSM would describe the distribution of a random variable V = V 1 , .


To score an unaligned pair, it sums over all possible alignments.

our WFST topology and parameterization follow the state-of-theart approach to supervised morphology in Dreyer et al.


our mining algorithm selects the most relevant features based on SVM estimated weights and uses this information to automatically infer an explicit representation of the input data.


(Rakotomamonjy, 2003;Weston et al., 2003;Kudo and Matsumoto, 2003).

we provided algorithmic solutions to deal with the huge dimensionality and, consequently, high computational complexity of the fragment space.


The results show that: (a) on large data sets, our approach can improve training and test time while yielding almost unaffected classification accuracy, and (b) our framework can effectively exploit the ability of TKs and SVMs to, respectively, generate and recognize relevant structured features.

we (i) study in more detail the relevant fragments identfied for the boundary classification task of SRL, (ii) closely observe the most relevant fragments for each QC class and (iii) look at the diverse syntactic patterns characterizing each ques-tion category.


the L-th ranked fragment at the step k + 1, and its score are the same as at the end of the previous iteration.

we assume that if none of the fragments mined during the (k + 1)-th iteration managed to affect the bottom of the pool of the L most relevant fragments, then none of their expansions is likely to succeed.


Training BC took more than two days of CPU time and testing about 4 hours, while training and testing the linearized boundary classifier required only 381 and 25 minutes, respectively.

on the same amount of data we can train a linearized classifier about 8 times as fast, and test it in about 1 tenth of the time.


how to define an event sentence is not obvious.


There have been many studies on sentiment classification and opinion summarization Lee, 2004, 2005;Gamon et al., 2005;Popescu and Etzioni, 2005;Liu et al., 2005;Zhuang et al., 2006;Kim and Hovy, 2006).

aspect rating as an interesting topic has also been widely studied (Titov and McDonald, 2008a;Snyder and Barzilay, 2007;Goldberg and Zhu, 2006).


݈ܲሺ݆ܽ݀ ௧ ሻ represents the polarity of ݆ܽ݀ ௧ , assigned as 1 if ݆ܽ݀ ௧ is positive, and -1 if negative.

negations are well handled by the same scoring strategy, treated exactly the same as modifying adverbs, except that they get such strong negative scores that the sentiment of the associated adjectives is pushed to the other side of the polarity scale.


In this paper, we first investigate ways in which an opinion discourse scheme can be computationally modeled, and then how it can be utilized to improve polarity classification.

the discourse scheme we use is from Somasundaran et al.


Thus, we build Local using a variety of knowledge sources that have been shown to be useful for opinion analysis in previous work.

we construct features using polarity lexicons (used by (Wilson et al., 2005)), DA tags (used by (Somasundaran et al., 2007)) and unigrams (used by many researchers, e.g., (Pang and Lee, 2004)).


we determine whether a conditional sentence (which is also called a conditional in the linguistic literature) expresses positive, negative or neutral opinions on some topics/features.


We borrow machine learning features which have been successfully used in WSD.

given an ambiguous target word, we use the following features from (Mihalcea, 2002): CW : the target word itself CP : POS of the target word CF : surrounding context of 3 words and their POS HNP : the head of the noun phrase to which the target word belongs NB : the first noun before the target word VB : the first verb before the target word NA : the first noun after the target word VA : the first verb after the target word SK : at most 10 context words occurring at least 5 times; determined for each sense Our target words are members of a subjectivity lexicon, because, since they are in such a lexicon, we know they have subjective usages.


Specifically, given an ambiguous target word, we use the following features from (Mihalcea, 2002): CW : the target word itself CP : POS of the target word CF : surrounding context of 3 words and their POS HNP : the head of the noun phrase to which the target word belongs NB : the first noun before the target word VB : the first verb before the target word NA : the first noun after the target word VA : the first verb after the target word SK : at most 10 context words occurring at least 5 times; determined for each sense Our target words are members of a subjectivity lexicon, because, since they are in such a lexicon, we know they have subjective usages.

we use the lexicon of (Wilson et al., 2005b;Wilson, 2008).


The alignment information in s-phrases will be useful in tying syntactic dependencies created in the target language to positions in the source language string.

we will consider discriminative models (analogous to models for dependency parsing, e.g., see (McDonald et al., 2005)) that estimate the probability of targetlanguage dependencies conditioned on properties of the source-language string.


Alignments may be derived in a number of ways; in our method we directly use phrase entries proposed by a phrasebased system.

for each target word e i in a phrase entry f 1 .


Given a sentence s and its parse tree τ s , we formulate the translation problem as finding the target sentence t * (along with its parse tree τ * t and alignment a * to the source tree) such that 3 In order to include overlapping features and permit hidden variables during training, we use a single globally-normalized conditional log-linear model.

where the g are arbitrary feature functions and the θ are feature weights.


Our first set of experiments compares feature sets commonly used in phrase-and syntax-based translation.

we compare the effects of combining phrase features and syntactic features.


Our Modeler adopts a supervised ML regression model.

we use a Gradient Boosted Decision Tree regression model -GBDT (Friedman, 2001), which consists of an ensemble of decision trees, fitted in a forward step-wise manner to current residuals.


Although LDA is expressive enough to model multiple topics per document, it is not appropriate for multi-labeled corpora because, as an unsupervised model, it offers no obvious way of incorporating a supervised label set into its learning procedure.

lDA often learns some topics that are hard to interpret, and the model provides no tools for tuning the generated topics to suit an end-use application, even when time and resources exist to provide some document labels.


Towards this objective, we first generate the document's labels Λ (d) using a Bernoulli coin toss for each topic k, with a labeling prior probability Φ k , as shown in step 5.

next, we define the vector of document's labels to be λ (d)  This allows us to define a document-specific label projection matrix the i th row of L (d) has an entry of 1 in column j if and only if the i th document label λ is equal to the topic j, and zero otherwise.


Let T be the set of all one to three word terms in the language, T d ⊂ T the set of discovered terms, T c ⊂ T the set of all capitalized terms and P at(t 1 , t 2 ) indicates one or more co-appearances of t 1 and t 2 in pattern P at in the retrieved snippets or offline general corpus.

the rank R of pattern P at is defined by: we rank patterns according to the percentage of capitalized words connected by this pattern.


In this paper, we address the issue of automatic extending lexical resources by exploiting existing knowledge repositories.

we deal with the new task of linking FrameNet and Wikipedia using a word sense disambiguation system that, for a given pair frame-lexical unit (F, l), finds the Wikipage that best expresses the the meaning of l. The mapping can be exploited to straightforwardly acquire new example sentences and new lexical units, both for English and for all languages available in Wikipedia.


While similar works in the past have mainly proposed to automatically extend the FrameNet database by mapping frames and Word-Net synsets (Shi and Mihalcea (2005), Johansson and Nugues (2007), and Tonelli and Pighin (2009)), we present an explorative approach that for the first time exploits Wikipedia to this purpose.

given a lexical unit l belonging to a frame F , we devise a strategy to link l to the Wikipedia article that best captures the sense of l in F .


The algorithm proved effective at Senseval-3 (Mihalcea and Edmonds, 2004) and, nowadays, it still represents the state-of-the-art in WSD (Pradhan et al., 2007).

they addressed these issues: (i) independently modeling domain and syntagmatic aspects of sense distinction to improve feature representativeness; and (ii) exploiting external knowledge acquired from unlabeled data, with the purpose of drastically reducing the amount of labeled training data.


(2005) modified the generic definition of the string kernel in order to take into account (sparse) collocations.

they defined syntagmatic kernels as a combination of string kernels applied to sequences of words in a fixed-size window centered on the word to be disambiguated.


Both (F, l) pairs and the assigned sentences were randomly selected.

the 20 (F, l) pairs do not contain only correctly assigned pairs, in fact three of them are wrong.


This paper discusses experiments that measure the effectiveness of machine-aided annotation for language documentation using both active learning simulation experiments and annotation experiments which involve actual documentary linguists interacting with machine example selection and label suggestion.

we deal with the task of labeling morphemes of the Mayan language Uspanteko with fine-grained parts-ofspeech.


2 We use a new related measure, which we call the overall percentage error reduction (OPER), that uses the entire area under the curves given by fitted nonlinear regression models rather than averaging over a subset of data points.

we fit a modified Michaelis-Menton model: The (original) parameters V m and K respectively correspond to the horizontal asymptote and the cost where accuracy is halfway between 0 and V m .


we looked at: (i) combining the two classifiers without training set enlargement or boosting of the literal class (combined), (ii) boosting the literal class with 200 automatically labelled non-canonical form examples (combined+boost), (iii) enlarging the training set by iteration (com-bined+it), and (iv) enlarging the training set by iteration and boosting the literal class after each iteration (combined+boost+it).


Without loss of generality, we will assume that for every rule A → α ∈ P , one of two cases holds: either RHS(A → α) ∈ T or RHS(A → α) ∈ V * .

the right-hand side sequences consist of either (1) exactly one terminal item, or (2) zero or more non-terminals.


The alignment variable may be empty, a v = ∅, while non-empty values must obey the tree constraints.

the span must be a subspan of its ancestor, a v ⊆ a p , subsume its descendants, a v ⊇ a d , and not overlap its siblings, j ∈ a s , ∀j ∈ a v .


The LNLZ08 system models p(T ( w, m)), the joint generative process for the hybrid tree containing both NL and MR.

this term can be rewritten in the following way: we reach an alternative view of the joint generative process as follows.


We define an operation yield(T ) which returns the NL sentence as the yield of the hybrid tree T .

given an MR m, we find the most probable NL sentence w * as follows: we first find the most probable hybrid tree T that contains the provided MR m. Next we return the yield of T as the most probable NL sentence.


The current work investigates several representations of source documents.

an approach from the literature based on atomic events (Filatova and Hatzivassiloglou, 2004) is compared to a novel approach based on generic relation extraction (GRE), which aims to build systems for relation identification and characterisation that can be transferred across domains and tasks without modification of model parameters (Hachey, 2009).


These are identical to the EV and RL representations above, except they are <Ent, Ent> 2-tuples instead of <Ent, Connector, Ent> 3-tuples.

entity pairs are included here provided that they meet the relation mention identification constraints.


Inspection of the corresponding document sets suggests that the different approaches compared here are appropriate for different types of summary tasks.

it suggests that relation and event representations perform poorly on summarisation tasks that are oriented towards sentiment, description or analysis.


In this paper we propose a novel statistical language model to capture long-range semantic dependencies.

we apply the concept of semantic composition to the problem of constructing predictive history representations for upcoming words.


To do this, we combine, via some (additive or multiplicative) function f , the vector representing that word with the vector representing the history up to n − 1 words: One issue that must be resolved in implementing equation 14is that the history vector should remain correctly normalized.

the products h i • p(c i ) must themselves be a valid distribution over context words.


These results, together with the basic mathematical structure of the LDA model, suggest that it may not be well suited to forming representations for word sequences.

the assumption that words are generated independently within documents prevents the interactions between words being modeled.


For each prepositional phrase that has been identified as an argument of the predicate, we jointly infer its semantic role and the sense of the preposition that is the lexical head of the prepositional phrase.

our model maximizes the joint probability of the semantic role and the preposition sense.


In this paper, we focus on the classification phase.

we assume that prepositional phrases that are semantic arguments have been identified correctly and concentrate on the task of determining the semantic role of prepositional phrases.


This means that their experiments have not included all types of sentences that might be encountered in practice when attempting MWE identifi-cation.

they would miss many examples in which the MWE words are present but are not used as an expression (case (b) in Section 1).


In our approach there is no pre-processing stage requiring type-specific knowledge.

the above examples are used as training sentences in our experiments.


The results in the third row show that the NCF+Dice system outperforms the methods based on GIZA++.

the NCF method can effectively resolve the difficulties of extracting MWE translations discussed in Section 1.


When the relevance labels are assigned by humans, it is reasonable to assume that queries with the lowest information entropy of labels are the least noisy.

for such a query most of the URLs are labeled as highly relevant/not relevant documents rather than as moderately relevance/not relevant documents.


However, 1 Note that in a sense our proposed LambdaBoost algorithm is the same as LambdaSMART, but using a single feature at each iteration, rather than a tree.

they share the trick of using the Lambda one problem of trees is their high variance.


Interestingly, model interpolation, though simple, leads to the two best rankers on the open test set.

the 3-way interpolated ranker outperforms the two baseline rankers  (Table 2).


We design special inference algorithms, instead of general-purpose inference algorithms used in previous works Ding et al., 2008), by taking advantage of special properties of our task.

we utilize two special properties of thread structure to reduce the inference (time) cost.